This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.

Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Ctrl+Shift+Enter. # Cellchat

source("./tianfengRwrappers.R")
library(CellChat)
ds2 <- readRDS("ds2.rds")
CA_dataset2 <- readRDS("CA_dataset2.rds")
# saveRDS(CA_dataset2,"CA_dataset2.rds")
umapplot(CA_dataset2)
Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
Please use `as_label()` or `as_name()` instead.
This warning is displayed once per session.

f("DLX5",EC_SMC)
umapplot(EC_SMC)

saveRDS(EC_SMC,"EC_SMC.rds")

CellChat

配体-受体+辅因子

computeAveExpr(cor, features = c("BMP2","BMP4","BMP6","BMPR1B","BMPR1A","BMPR2","ACVR2A"), type =  "truncatedMean", trim = 0.05)
                3        EC0         EC1         EC2 Fibroblast Fibromyocyte    Pericyte        SMC1       SMC2
BMP2   0.03150669 0.02623810 0.003702709 0.202131802 0.00000000   0.00000000 0.028212306 0.000000000 0.00000000
BMP4   0.00000000 0.02969807 0.064427142 0.535072542 0.14289603   0.05976591 0.000000000 0.008078115 0.02235959
BMP6   0.33885091 0.00000000 0.000000000 0.430656995 0.00000000   0.00000000 0.000000000 0.000000000 0.00000000
BMPR1B 0.00000000 0.00000000 0.000000000 0.000000000 0.08194540   0.00000000 0.000000000 0.008751292 0.07602259
BMPR1A 0.00000000 0.00000000 0.000000000 0.006539124 0.05175499   0.03366126 0.009241962 0.057668768 0.02832214
BMPR2  0.30466733 0.28211628 0.328085107 0.880202371 0.11521646   0.13015395 0.078799890 0.157702091 0.11248721
ACVR2A 0.00000000 0.00000000 0.000000000 0.000000000 0.01848392   0.00000000 0.000000000 0.000000000 0.00000000
write.csv(df.net,"./datatable/EC_SMC_cellchat_res.csv", row.names = F)

cellchat可视化

cor <- readRDS("EC_SMC_cellchat.rds")

# cor@idents <- plyr::revalue(cor@idents, c("Mod_SMC"="Fibroblast", "SMC_0"="SMC1","SMC_1"="Fibromyocyte",
#                    "SMC_2"="Pericyte","SMC_3"="SMC2"))

table(cor@idents)
groupSize <- as.numeric(table(cor@idents))
netVisual_circle(cor@net$weight, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Interaction weights/strength")
netVisual_heatmap(cor, measure = "weight", signaling = NULL, color.heatmap = c("#f1f1f1", "#ff2121"))

#一次分析不同细胞亚群
mat <- cor@net$weight
for (i in 1:nrow(mat)) {
  mat2 <- matrix(0, nrow = nrow(mat), ncol = ncol(mat), dimnames = dimnames(mat))
  mat2[i, ] <- mat[i, ]
  netVisual_circle(mat2, vertex.weight = groupSize, weight.scale = T, edge.weight.max = max(mat), title.name = rownames(mat)[i])
}

# pheatmap(mat, display_numbers = FALSE, number_color ="black", cluster_rows = FALSE, 
         # cluster_cols = FALSE, color = colorRampPalette(c("#1E90FF", "white", "#ff2121"))(400), border_color = NA)

netVisual_aggregate(cor, signaling = pathways.show, layout = "circle")

netVisual_bubble(cor, sources.use = 4, targets.use = c(5:9), pairLR.use = LR_show, remove.isolate = F) #EC2 target
Comparing communications on a single object 

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---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 
# Cellchat
```{r}
source("./tianfengRwrappers.R")
library(CellChat)
```


```{r}
ds2 <- readRDS("ds2.rds")
CA_dataset2 <- readRDS("CA_dataset2.rds")
# saveRDS(CA_dataset2,"CA_dataset2.rds")
umapplot(CA_dataset2)
```

```{r}
EC_SMC <- subset(CA_dataset2, idents = c("Endothelial cell"))

EC_SMC <- EC_SMC %>% FindNeighbors(dims = 1:20) %>% RunUMAP(dims = 1:20) %>% 
  FindClusters(resolution = 0.1)

umapplot(EC_SMC)
EC_SMC <- RenameIdents(EC_SMC,'0' = 'EC0','1' = 'EC1','2' = 'EC2')
Dotplot(c("BMP1","BMP2"),EC_SMC)

EC_SMC <- merge(EC_SMC, ds2)
EC_SMC$Classification2 <- Idents(EC_SMC)
EC_SMC <- EC_SMC %>% PercentageFeatureSet(pattern = "^MT-", col.name = "percent.mt") %>%
  SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
  RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
  RunUMAP(dims = 1:20)

f("ACVR2A",EC_SMC)
umapplot(EC_SMC)
saveRDS(EC_SMC,"EC_SMC.rds")
```

# CellChat
## 配体-受体+辅因子
```{r}
cor <- createCellChat(EC_SMC)
cellchatDB <- CellChatDB.human

showDatabaseCategory(cellchatDB)
#cellchatDB_use <- subsetDB(cellchatDB, search = "Secreted Signaling")  #使用特定类型的配体受体相互关系

#指定分析使用的database
cor@DB <- cellchatDB

#节省计算成本
cor <- subsetData(cor)
cor <- identifyOverExpressedGenes(cor)
cor <- identifyOverExpressedInteractions(cor)
#使用蛋白质互作网络，根据基因间相互关系补充表达量
# cor <- projectData(cor, PPI.human)

computeAveExpr(cor, features = c("BMP2","BMP4","BMP6","BMPR1B","BMPR1A","BMPR2","ACVR2A"), type =  "truncatedMean", trim = 0.05)
#推断细胞间通信网络，不要使用并行计算！
cor <- computeCommunProb(cor, type = "truncatedMean", trim = 0.05)
#若细胞群中只有少数细胞出现联系(<10个)，则过滤掉它们
cor <- filterCommunication(cor, min.cells = 10)

#提取表达网络矩阵
# df.net <- subsetCommunication(cor)
df.net <- subsetCommunication(cor,signaling = c("BMP")) #指定分析的通路和细胞类群

#推断信号通路
cor <- computeCommunProbPathway(cor)

#合并通信网络数据
cor <- aggregateNet(cor)
saveRDS(cor,"EC_SMC_cellchat.rds")
```

```{r}
write.csv(df.net,"./datatable/EC_SMC_cellchat_res.csv", row.names = F)
```



## cellchat可视化
```{r,fig.height=6,fig.width=6}
cor <- readRDS("EC_SMC_cellchat.rds")

# cor@idents <- plyr::revalue(cor@idents, c("Mod_SMC"="Fibroblast", "SMC_0"="SMC1","SMC_1"="Fibromyocyte",
#                    "SMC_2"="Pericyte","SMC_3"="SMC2"))

table(cor@idents)
groupSize <- as.numeric(table(cor@idents))
netVisual_circle(cor@net$weight, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Interaction weights/strength")
netVisual_heatmap(cor, measure = "weight", signaling = NULL, color.heatmap = c("#f1f1f1", "#ff2121"))

#一次分析不同细胞亚群
mat <- cor@net$weight
for (i in 1:nrow(mat)) {
  mat2 <- matrix(0, nrow = nrow(mat), ncol = ncol(mat), dimnames = dimnames(mat))
  mat2[i, ] <- mat[i, ]
  netVisual_circle(mat2, vertex.weight = groupSize, weight.scale = T, edge.weight.max = max(mat), title.name = rownames(mat)[i])
}

# pheatmap(mat, display_numbers = FALSE, number_color ="black", cluster_rows = FALSE, 
         # cluster_cols = FALSE, color = colorRampPalette(c("#1E90FF", "white", "#ff2121"))(400), border_color = NA)

```

```{r,fig.height=6,fig.width=6}
netVisual_chord_gene(cor, sources.use = c(4), targets.use = c(2:9), lab.cex = 0.5, legend.pos.y = 30,thresh = 0.001)

netVisual_chord_gene(cor, sources.use = c(4), targets.use = c(9), lab.cex = 0.5, legend.pos.y = 30, thresh = 0.001)
```


```{r,fig.height=6,fig.width=6}
#指定pathway
pathways.show <- c("BMP") 

netVisual_aggregate(cor, signaling = pathways.show, layout = "circle")
# netVisual_aggregate(cor, signaling = pathways.show, layout = "chord")

# Heatmap
netVisual_heatmap(cor, signaling = pathways.show, color.heatmap = c("#f1f1f1", "#ff2121"))
```


```{r,fig.height=4,fig.width=4}
#可视化单个配体受体对的影响
netAnalysis_contribution(cor, signaling = pathways.show)
pairLR.BMP <- extractEnrichedLR(cor, signaling = pathways.show, geneLR.return = FALSE,thresh = 0.001)
LR.show <- pairLR.BMP[8,] # show one ligand-receptor pair BMP2_BMPR1B
netVisual_individual(cor, signaling = pathways.show, layout = "hierarchy",  pairLR.use = LR.show, vertex.receiver = c(5:9))
netVisual_individual(cor, signaling = pathways.show, pairLR.use = LR.show, layout = "circle")
# netVisual_individual(cor, signaling = pathways.show, pairLR.use = LR.show, layout = "chord")

LR.show <- pairLR.BMP[2,] # show one ligand-receptor pair BMP2_BMPR1B
netVisual_individual(cor, signaling = pathways.show, layout = "hierarchy",  pairLR.use = LR.show, vertex.receiver = c(5:9))

# Hierarchy plot
netVisual_individual(cor, signaling = pathways.show,layout = "hierarchy",  pairLR.use = LR.show, vertex.receiver = vertex.receiver)
netVisual_individual(cor, signaling = pathways.show, pairLR.use = LR.show, layout = "circle")
netVisual_individual(cor, signaling = pathways.show, pairLR.use = LR.show, layout = "chord")

LR_show <- data.frame(interaction_name = pairLR.BMP[c(2,4,6,8),]) #展示BMP作为配体

netVisual_bubble(cor, sources.use = 4, targets.use = c(5:9), pairLR.use = LR_show, remove.isolate = F, return.data = T) #EC2 target

```
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The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.
